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相关概念视频

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Relative Risk01:12

Relative Risk

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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
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Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
471
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Classification of Illness01:17

Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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相关实验视频

Updated: Jan 17, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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CTBN-PH:用于个性化诊断风险预测的连续时间贝叶斯网络.

Guillem Hernández Guillamet1, Francesc López Seguí2, Josep Vidal Alaball3

  • 1eXiT Research Group, Universitat de Girona (UdG), EPS - Edifici P-IV, Carrer Universitat de Girona, 6, Girona, 17003, Catalunya, Spain; Assistance strategy management. Hospital Germans Trias i Pujol, (ICS), Carretera de Canyet, Badalona, 08916, Catalunya, Spain; Research Group on Innovation, Health Economics and Digital Transformation, Institut Germans Trias i Pujol (IGTP), Cami de les Escoles, Badalona, 08916, Catalunya, Spain.

Computers in biology and medicine
|September 24, 2025
PubMed
概括

本研究介绍了CTBN-PH模型,将连续时间贝叶斯网络与Cox比例危险模型集成在一起,用于个性化的疾病轨迹预测. 该模型有效地捕捉了医疗保健数据中的复杂因果结构和个体患者风险因素.

关键词:
因果推理的原因推理.连续时间贝叶斯网络考克斯的比例危险性疾病建模 疾病建模个性化的风险个性化的风险

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科学领域:

  • * 计算生物学 * 计算生物学
  • * 医学信息学 医学信息学
  • * 生物统计学

背景情况:

  • *连续时间贝叶斯网络 (CTBNs) 提供疾病进展的动态建模,但往往缺乏患者特定的预测.
  • *共变量显著影响诊断过渡,对标准CTBN应用构成挑战.
  • *将共变效应整合到CTBN中对于个性化疾病轨迹预测至关重要.

研究的目的:

  • * 引入CTBN-PH模型,将CTBN和Cox比例危险 (Cox-PH) 模型结合起来.
  • *通过结合共变效应,使复杂疾病轨迹的个性化预测成为可能.
  • *利用医疗保健数据的因果拓来进行动态风险估计.

主要方法:

  • * 整合CTBN与Cox-PH模型,形成CTBN-PH模型.
  • *从广泛的医疗保健轨迹 (超过210万患者) 中学习因果拓.
  • *基于个人化风险评估的共同变量效应的过渡强度的动态调整.

主要成果:

  • * CTBN-PH模型成功地学习了与糖尿病和高血压等多病症相关的复杂因果结构.
  • * 获得了0.153的综合障碍得分 (IBS),用于预测25年内一次性诊断的发作.
  • *与非个性化模型相比,在预测系统惯性 (四年内IBS为0.04) 中表现强.
  • * 在模拟针对特定的共同变量定义的群体量身定制的患者轨迹方面得到验证的实用性.

结论:

  • * CTBN-PH模型为个性化疾病轨迹预测提供了一个强大的框架.
  • *结合因果推断和共变效应,可显著提高预测准确度.
  • * 该模型在临床决策支持和个性化医学模拟中提供了有价值的应用.